Multi-GPU based framework for real-time motion analysis and tracking in multi-user scenarios

Mahmoudi, Sidi Ahmed

doi:10.4108/ct.2.2.e5

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The framework was exploited for implementing different GPU‐based video processing applications in real time such as event detection and event localization, motion detection within mobile camera. More details about these implementations are present in Mahmoudi . Notice that this framework is not fully integrated within our cloud platform.…”

Section: Resultsmentioning

confidence: 99%

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Towards a smart selection of resources in the cloud for low‐energy multimedia processing

Mahmoudi

Belarbi

Mahmoudi

et al. 2017

Concurrency and Computation

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Summary Nowadays, image and video processing applications have become widely used in many domains related to computer vision. Indeed, they can come from cameras, smartphones, social networks, or from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc) in many situations such as airports, train stations, public areas, sport events, and hospitals. Thus, image and video processing algorithms have got increasing importance, they are required from various computer vision applications such as motion tracking, real time event detection, database (images and videos) indexation, and medical computer‐aided diagnosis methods. The main inconvenient of image and video processing applications is the high intensity of computation and the complex configuration and installation of the related materials and libraries. In this paper, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in a cloud‐based platform, in case of processing one image (or one video in real time) or many images (or videos). This framework enables to affect the local or remote computing units for calculation after analyzing the type of media and the algorithm complexity. The framework disposes of a set of selected CPU and GPU‐based computer vision methods, such as image denoising, histogram computation, features descriptors (SIFT, SURF), points of interest extraction, edges detection, silhouette extraction, and sparse and dense optical flow estimation. These primitive functions are exploited in various applications such as medical image segmentation, videos indexation, real time motion analysis, and left ventricle segmentation and tracking from 2D echocardiography. Experimental results showed a global speedup ranging from 5× to 273×(compared to CPU versions) as result of the application of our framework for the above‐mentioned methods. In addition to these performances, the parallel and heterogeneous implementations offered lower power consumption as result of the fast treatment.

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Section: Resultsmentioning

confidence: 99%

“…In this section, we describe our image() and video() primitive functions that could be exploited within the proposed cloud framework for accelerating a set of selected computer vision methods.…”

Section: Gpu‐based Primitive Functionsmentioning

confidence: 99%

Towards a smart selection of resources in the cloud for low‐energy multimedia processing

Mahmoudi

Belarbi

Mahmoudi

et al. 2017

Concurrency and Computation

Self Cite

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Recent Advances in Object Detection and Tracking for High Resolution Video: Overview and State-of-the-Art

Rajjak

Kureshi

2019

2019 5th International Conference on Computing, Communication, Control and Automation (ICCUBEA)

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Multiple-Object Detection and Segmentation Based on Deep Learning in High-Resolution Video Using Mask-RCNN

Rajjak

Kureshi

2021

Int. J. Patt. Recogn. Artif. Intell.

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Imaging sensors with higher resolution and higher frame rates are becoming more popular for wide-area video surveillance (VS) and other applications as technology advances Using Mask-RCNN, we proposed Multiple-Object Detection and Segmentation in High-Resolution Video based on Deep Learning. The ResNet-50 ResNet-101 is used as the backbone in the proposed R-CNN Mask FPN model. The deep residual network’s design overcomes the problem of lower learning efficiency due to the network’s deepening. To reach the objective of the smallest overall error, the deep residual network divided the training series into one training block, minimizing the error of each block. It is roughly divided into five convolutional layer stages. The output scale is cut in half at each point. We used mixed precision FP16 and FP32 for training the model and achieved great speed in training time reduction in inference time for object. The COCO 2014 data set is used to train and validate the proposed model with mixed precision, leading to faster performance. The results of the experiments show that the proposed model can run at 30–48 frames per second with 85% accuracy.

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Multi-GPU based framework for real-time motion analysis and tracking in multi-user scenarios

Cited by 4 publications

References 10 publications

Towards a smart selection of resources in the cloud for low‐energy multimedia processing

Towards a smart selection of resources in the cloud for low‐energy multimedia processing

Recent Advances in Object Detection and Tracking for High Resolution Video: Overview and State-of-the-Art

Multiple-Object Detection and Segmentation Based on Deep Learning in High-Resolution Video Using Mask-RCNN

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